Alzheimer's disease (AD) presently affects over 5 million Americans and is projected to affect 15 million by 2050. Biomarkers are presently the only feasible approach for diagnosing and quantifying disease-associated changes in the latent AD stage during which a successful disease-modifying therapeutic intervention would realize the greatest impact. High-throughput neuroimaging and genetics have a proven track record for critically advancing our understanding of disease mechanisms and promoting therapeutic development. Our goals are to develop a multimodal biomarker AD risk assessment tool using the prospectively collected imaging, genetic and gene expression ImaGene data set. We propose to apply advanced imaging genetics statistical approaches to achieve the following three aims: 1) identify a discovery set of AD-relevant candidate imaging and genetic biomarkers; 2) select gene expression variables with strong evidence for biological relevance to AD; and 3) develop and validate a multimodal classifier capable of accurately assessing one's risk for future conversion to AD. The discovery of critical disease-related pathways will fundamentally advance our understanding of the molecular and genetic triggers of AD and bring us closer to genomic-based interventions and personalized risk assessment.